Assessment of the accuracy of scaling methods for radiance simulations at far and mid infrared wavelengths

Martinazzo, Michele; Magurno, D.; Cossich, William; Serio, Carmine; Masiello, Guido; Maestri, Tiziano

doi:10.1016/j.jqsrt.2021.107739

Cited by 15 publications

(16 citation statements)

References 58 publications

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“…However, many following studies have shown the importance of FIR and its strength in remote sensing the atmospheric properties in the upper troposphere (e.g., Harries et al., 2008). For example, several studies showed that a large fraction of the OLR energy lies in the FIR region (Bellisario et al., 2019; Chen et al., 2014; Harries et al., 2008; X. Huang et al., 2008; Martinazzo et al., 2021; Turner & Mlawer, 2010). Moreover, as the OLR in FIR is especially sensitive to atmospheric water vapor and clouds (e.g., cirrus), investigations and observations in FIR could potentially improve the retrieval of their information (Blanchet et al., 2011; Clough et al., 1992; Cox et al., 2015; Delamere et al., 2010; Harries et al., 2008; Libois & Blanchet, 2017; Maestri & Rizzi, 2003; Magurno et al., 2020; Palchetti et al., 2015, 2016; Sinha & Harries, 1995).…”

Section: Introductionmentioning

confidence: 99%

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

Huang

2022

JGR Atmospheres

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The outgoing longwave radiation (OLR), which consists of the thermal radiation from both the atmosphere and surface, is of critical importance to the Earth radiation energy budget. To understand the global OLR distribution, it is important to quantify the varying atmospheric and surface contributions. In this work, we present such a quantification using radiative transfer computations based on global reanalysis atmospheric data. By dissecting the OLR simulated following the radiative transfer equation, we quantitatively measure the layer‐wise atmospheric contributions to OLR and compare it to the surface contribution in different spectral bands. One focus of this study is on the OLR in the far‐infrared (FIR), for which new satellites are expected to provide unprecedented measurements. We find that around 45% of the global mean OLR is radiated in the FIR and in polar regions the FIR contribution can increase to 60%. Our vertical decomposition of OLR discloses that although the atmospheric contribution generally surpasses the surface contribution, the enhanced FIR contribution in the polar regions mainly results from a relatively stronger surface (as opposed to atmospheric) contribution. This is allowed by the opening of the atmospheric window in the FIR in these extremely dry regions. Our analysis also reveals that the tropopause layer makes a minimum contribution to the OLR, which may be a unique spectroscopic feature of the Earth atmosphere. Clouds are found to reduce the atmospheric contribution in the FIR while enhancing it in the mid‐infrared.

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Section: Introductionmentioning

confidence: 99%

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

Huang

2022

JGR Atmospheres

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“…Recommended RTTOV-SCATT optical properties have been used [25] including Mie spheres for rain and non-precipitating hydrometeors and sector snowflakes for solid precipitation. For the IR, the all-sky version of s-IASI (s-IASI_as) is used [26], [27], which exploits the Chou scaling approximation [28]. The optical properties for ice are those for ice cloud crystal habits aggregates and are taken from the LBLDIS [29] library.…”

Section: A Iasi-ng and Mws Instrumentsmentioning

confidence: 99%

Combined IASI-NG and MWS Observations for the Retrieval of Cloud Liquid and Ice Water Path: A Deep Learning Artificial Intelligence Approach

Mastro

Masiello

Serio

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A neural network (NN) approach is proposed to combine future infrared (IASI-NG) and microwave (MWS) observations to retrieve cloud liquid and ice water path. The methodology is applied to simulated IASI-NG and MWS observations in the period January-October 2019. IASI-NG and MWS observations are simulated globally at synoptic hours (00:00, 06:00, 12:00, 18:00 UTC) and on a regular spatial grid (0.125° × 0.125°) from ECMWF 5-generation reanalysis (ERA5). The state-of-the-art σ-IASI and RTTOV radiative transfer codes are used to simulate IASI-NG and MWS observations, respectively, from the Earth's state vector given by ERA5. A principal component (PC) analysis of the simulated IASI-NG observations is performed. Accordingly, a NN is developed to retrieve cloud liquid and ice water path from a combination of 24 MWS channels and 30 IASI-NG PCs. Validation indicates that this combination results in liquid and ice water path retrievals with overall accuracy of 1.85 10 -2 kg/m 2 and 1.18 10 -2 kg/m 2 , respectively, and 0.97 correlation with respect to reference values. The rmse for CLWP results in about 30% of the mean value (5.91 10 -2 kg/m 2 ) and 22% of the variability (1-sigma). Similarly, the rmse for CIWP results in about 41% of the mean value (2.91 10 -2 kg/m 2 ) and 22% of the variability. Two more NN are developed, retrieving cloud liquid and ice water path from microwave observations only (24 MWS channels) and infrared observations only (30 IASI-NG PCs), demonstrating quantitatively the advantage of using the combination of infrared and microwave observations with respect to either one alone.

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“…The IASI measurements we use in this work have been simulated with the latest version of 𝜎𝜎-IASI-as [17][18][19][20][21][22][23][24] radiative transfer model (RTM). The atmospheric state vector used for the simulation of IASI spectra is the Monitoring Atmospheric Composition and Climate (MACC) Reanalysis dataset 25,26 of the European Centre for Medium-Range Weather Forecasts (ECMWF).…”

Section: Datamentioning

confidence: 99%

Methane profile retrieval from IASI: a deep learning inversion approach based on feed-forward neural networks

Masiello

Mastro²,

Serio³

et al. 2022

Remote Sensing of Clouds and the Atmosphere XXVII

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In this work, a nonlinear statistical regressor method based on deep learning feed-forward neural network (NN) for the retrieval of atmospheric CH4 is proposed. The methodology has been trained and validated on a simulated dataset of observations by the processing of the Monitoring Atmospheric Composition and Climate (MACC) Reanalysis dataset with the state-of-the-art transfer model (RTM) σ-IASI-as. Global data related to one day of the 12 months of 2012 and four synoptic hours (00-06-12-18 UTC) have been processed to catch typical seasonal and diurnal cycles, corresponding to a fairly large number (168.000) of simulated IASI-L1 spectral radiances. CH4 profiles have been predicted on 60 pressure layers. A regression framework based on the principal components analysis (PCA) of the IASI radiances and CH4 profiles has been implemented. The choice of the number of principal components has been addressed by the study of their eigenvalues, to filter redundant information from IASI channels and extract the most significant information from the CH4 profiles. The analysis of the NN retrieval, shows agreement with the reference MACC CH4 contents, allowing to obtain unbiased profile estimates, with accuracy on the total content of about 1.55%. The same accuracy has been obtained for the tropospheric column while for the stratosphere atmospheric column the accuracy is about 3%. Finally, an additional analysis of the CH4 total content shows a correlation between the reference and predicted values of about 0.97.

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Assessment of the accuracy of scaling methods for radiance simulations at far and mid infrared wavelengths

Cited by 15 publications

References 58 publications

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

Combined IASI-NG and MWS Observations for the Retrieval of Cloud Liquid and Ice Water Path: A Deep Learning Artificial Intelligence Approach

Methane profile retrieval from IASI: a deep learning inversion approach based on feed-forward neural networks

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